Welcome to Modelling of Systems for Sustainability
On successful completion of this course, you should be able to:
- explain how computational modelling frameworks can be used to understand the behaviours of complex interacting systems involved in sustainability such as social, economic and ecological systems
- investigate a sustainability system question, identify system elements and their interactions, and codify a system model using an appropriate model description framework
- critique and interpret the results / output of models of sustainability systems
- communicate findings of sustainability modelling studies, including uncertainty, to a variety of audiences
- work collaboratively and accountably with other students to formulate, explore and communicate a sustainability system model
The fundamental aim of this course is for students from a variety of backgrounds, including Informatics, to get hands-on experience with specifying, implementing, exploring and presenting results from models of real-world systems that are key to planetary sustainability. The range of such systems is vast, encompassing large parts of earth sciences, engineering, health sciences, social and political sciences. Some key systems include the climate system, many ecosystems, agricultural systems, water systems, public health systems, social systems, international political systems, energy systems and transport systems. We will describe a subset of these systems, and there will be opportunity to develop deeper understanding in the project.
Most of these are what are known as "complex systems", meaning: their behaviour and evolution often cannot be reduced to a few equations or paragraphs of description; they exhibit patterns of emergent global behaviour that are not explicitly encoded in any local interactions; they often exhibit multiple potential stable states, with not-easily-triggered "tipping points" to move to another stable state; stability is often exhibited as a dynamic pattern over time rather than as a fixed state. We will touch on the science of complex systems, focussing on those aspects that are of most relevance to the particular real-world systems we study.
A ubiquitous challenge in sustainability is to appreciate how these individually complex systems interact with each other in the real world to produce unexpected outcomes. The key methodology we will bring to the study of these systems and how they interact is computational modelling and visualisation. We will study specific systems for which there are reasonably tractable computational models and visualisations. For each, we will explore the underlying computational framework, be it a statistical model of observed data, a physical model of known physical / chemical / biological interactions, or an abstract model of ecological or social systems. The modelling paradigms studied and used in practical work will include system dynamics and agent-based modelling, with brief mention of other paradigms such as discrete event and finite-step simulations.
The first half will consist of lecture material covering the key system concepts, the specific systems to be studied, and the computational modelling and visualisation methods used for each system. These will be supplemented by hands-on lab sessions, to explore computational models and visualisations using tools such as NetLogo and Python; and small-group tutorial sessions aimed at multidisciplinary discussions of specific systems. The second half of the semester will be devoted to a group project in which each 3/4-person group will have students from at least 2 and ideally 3 or 4 disciplines; the project will aim to construct and explore a model of one or two complex systems related to sustainability (e.g., energy, economic and political); and project supervision will be provided by staff and PhD students familiar with the project systems and models.
Assessment will be by coursework, aimed at the material covered in the first half of the semester, (systems and modelling paradigms) and a group project report and presentation for the work in the second half of the semester.
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